
import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCamelCase_    :			str    =	object()

# For specifying empty leaf dict `{}`
lowerCamelCase_    :			List[Any]    =	object()







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				int						=    tuple((re.compile(x + """$"""    ) for x in qs)    )
				for i in range(len(lowerCamelCase    ) - len(lowerCamelCase    ) + 1    ):
								UpperCamelCase_:				Dict						=    [x.match(lowerCamelCase    ) for x, y in zip(lowerCamelCase  ,     ks[i:]    )]
								if matches and all(lowerCamelCase    ):
												return True
				return False







def 							A__       (    lowerCamelCase    )	->						Optional[int]:
				def replace(lowerCamelCase  ,     lowerCamelCase    ):
								for rule, replacement in rules:
												if _match(lowerCamelCase  ,     lowerCamelCase    ):
																return replacement
								return val

				return replace







def 							A__       (    )	->						Dict:
				return [
				    # embeddings
				    (("transformer", "wpe", "embedding"), P("""mp"""  ,     lowerCamelCase    )),
				    (("transformer", "wte", "embedding"), P("""mp"""  ,     lowerCamelCase    )),
				    # atention
				    (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(lowerCamelCase  ,     """mp"""    )),
				    (("attention", "out_proj", "kernel"), P("""mp"""  ,     lowerCamelCase    )),
				    (("attention", "out_proj", "bias"), None),
				    # mlp
				    (("mlp", "c_fc", "kernel"), P(lowerCamelCase  ,     """mp"""    )),
				    (("mlp", "c_fc", "bias"), P("""mp"""    )),
				    (("mlp", "c_proj", "kernel"), P("""mp"""  ,     lowerCamelCase    )),
				    (("mlp", "c_proj", "bias"), None),
				    # layer norms
				    ((r"ln_\d+", "bias"), None),
				    ((r"\d+", r"ln_\d+", "scale"), None),
				    (("ln_f", "bias"), None),
				    (("ln_f", "scale"), None),
				]







def 							A__       (    lowerCamelCase    )	->						List[Any]:
				UpperCamelCase_:				Optional[Any]						=    _get_partition_rules()
				UpperCamelCase_:				Optional[int]						=    _replacement_rules(lowerCamelCase    )
				UpperCamelCase_:				List[str]						=    {k: _unmatched for k in flatten_dict(lowerCamelCase    )}
				UpperCamelCase_:				List[str]						=    {k: replace(lowerCamelCase  ,     lowerCamelCase    ) for k, v in initd.items()}
				assert _unmatched not in result.values(), "Incomplete partition spec."
				return freeze(unflatten_dict(lowerCamelCase    )    )








import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






